As we define professional malicious users in recommender systems, malicious user detection is a new problem, which is an issue with little attention yet. However, we can treat this detection issue as a special case of abnormal user detection, and some existing works in this area can inspire us [1]}, [2]}. In e-commerce, various abnormal users (spammers, shilling group, and frauds) have greatly damaged the systems, and some abnormal user detection models are proposed to tackle this issue. [3]} proposed a hybrid model to detect the spammers through users' profile and relations. [4]} explored spammer detection in big and sparse data. Shilling attacks harm the recommender system by injecting fake profile information of users and items. They cheat the recommendation model, such as Collaborative Filtering and Matrix Factorization [5]}. [6]} proposed this attack type and gave a basic supervised solution to tackle it. [5]} proposed a convolutional neural network to solve shilling attacks and improved collaborative filtering. Frauds usually give fake reviews to hurt the profits of electronic retailers. [8]}, [9]} also explored fraud detection in large-scale dataset and real scenarios. Some researches of abnormal user detection utilize the machine learning model to find fake ratings or reviews [10]}, [11]} and achieve an effective result.
